Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks

• URL: http://arxiv.org/abs/2002.09623v1
• Date: Sat, 22 Feb 2020 04:28:00 GMT
• Title: Anypath Routing Protocol Design via Q-Learning for Underwater Sensor Networks
• Authors: Yuan Zhou, Tao Cao, and Wei Xiang
• Abstract summary: This paper proposes a Q-learning-based localization-free anypath routing protocol for underwater sensor networks. The Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes. A mathematical analysis is presented to analyze the performance of the proposed routing protocol.
• Score: 12.896530402853612
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As a promising technology in the Internet of Underwater Things, underwater sensor networks have drawn a widespread attention from both academia and industry. However, designing a routing protocol for underwater sensor networks is a great challenge due to high energy consumption and large latency in the underwater environment. This paper proposes a Q-learning-based localization-free anypath routing (QLFR) protocol to prolong the lifetime as well as reduce the end-to-end delay for underwater sensor networks. Aiming at optimal routing policies, the Q-value is calculated by jointly considering the residual energy and depth information of sensor nodes throughout the routing process. More specifically, we define two reward functions (i.e., depth-related and energy-related rewards) for Q-learning with the objective of reducing latency and extending network lifetime. In addition, a new holding time mechanism for packet forwarding is designed according to the priority of forwarding candidate nodes. Furthermore, a mathematical analysis is presented to analyze the performance of the proposed routing protocol. Extensive simulation results demonstrate the superiority performance of the proposed routing protocol in terms of the end-to-end delay and the network lifetime.

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